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Clinical dialogue transcription error correction using Seq2Seq models.

Nanayakkara, Gayani; Wiratunga, Nirmalie; Corsar, David; Martin, Kyle; Wijekoon, Anjana

Authors



Contributors

Arash Shaban-Nejad
Editor

Martin Michalowski
Editor

Simone Bianco
Editor

Abstract

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decision-making regarding the patient and plays a crucial role in their healthcare journey. The reliance on note taking and manual scribing processes are extremely inefficient and leads to manual transcription errors when digitizing notes. Automatic Speech Recognition (ASR) plays a significant role in speech-to-text applications, and can be directly used as a text generator in conversational applications. However, recording clinical dialogue presents a number of general and domain-specific challenges. In this paper, we present a seq2seq learning approach for ASR transcription error correction of clinical dialogues. We introduce a new Gastrointestinal Clinical Dialogue (GCD) Dataset which was gathered by healthcare professionals from a NHS Inflammatory Bowel Disease clinic and use this in a comparative study with four commercial ASR systems. Using self-supervision strategies, we fine-tune a seq2seq model on a mask-filling task using a domain-specific PubMed dataset which we have shared publicly for future research. The BART model fine-tuned for mask-filling was able to correct transcription errors and achieve lower word error rates for three out of four commercial ASR outputs.

Citation

NANAYAKKARA, G., WIRATURNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2022. Clinical dialogue transcription error correction using Seq2Seq models. In Shaban-Nejad, A., Michalowski, M. and Bianco, S. (eds.) Multimodal AI in healthcare: a paradigm shift in health intelligence; selected papers from the 6th International workshop on health intelligence (W3PHIAI-22), co-located with the 34th AAAI (Association for the Advancement of Artificial Intelligence) Innovative applications of artificial intelligence (IAAI-22), 28 February - 1 March 2022, [virtual event]. Studies in computational intelligence, 1060. Cham: Springer [online], pages 41-57. Available from: https://doi.org/10.1007/978-3-031-14771-5_4

Conference Name 6th International workshop on health intelligence (W3PHIAI-22), co-located with the AAAI (Association for the Advancement of Artificial Intelligence) 34th Innovative applications of artificial intelligence (IAAI-22)
Conference Location [virtual event]
Start Date Feb 28, 2022
End Date Mar 1, 2022
Acceptance Date Dec 3, 2021
Online Publication Date Nov 29, 2022
Publication Date Dec 31, 2022
Deposit Date Oct 25, 2022
Publicly Available Date Nov 30, 2023
Publisher Springer
Pages 41-57
Series Title Studies in computational intelligence (SCI)
Series Number 1060
Series ISSN 1860-949X; 1860-9503
Book Title Multimodal AI in healthcare: a paradigm shift in health intelligence.
ISBN 9783031147708
DOI https://doi.org/10.1007/978-3-031-14771-5_4
Keywords Clinical dialogue transcription; Automatic speech recognition; Error correction
Public URL https://rgu-repository.worktribe.com/output/1686809
Related Public URLs https://rgu-repository.worktribe.com/output/1686647
Additional Information A pre-print version of this article was first available as: NANAYAKKARA, G., WIRATUNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2022. Clinical dialogue transcription error correction using Seq2Seq models. Hosted on arXiv [online]. Available from:
https://doi.org/10.48550/arXiv.2205.13572